Volume-04 , Special Issue-03 , May - 2016 Go Back

Wireless communication is the transfer of information over a distance without the use of wires. Wireless operations permit services, such as long-range communications that are impossible or impractical to implement using wires. Wireless communication brings fundamental changes to data networking and telecommunications, and makes integrated networks a reality. Network architecture for personal communication systems, wireless LANs, radio, tactical and other wireless networks, and design and analysis of protocols are addressed on a regular basis. At present, the major application of Wi-Fi implementation in libraries is limited to information management. This paper elaborates on new and upcoming technology like Gi-Fi and Li-Fi.

Wireless networking, Li-Fi, Gi-Fi, Critical Issues

Marzieh yazdanipour, Mina Yazdanipour, Afsaneh Yazdanipour, Amin Mehdipour,”Evaluation of Gi-Fi Technology for Short-Range, High-Rate Wireless Communication” UACEE International Journal ofAdvances in Computer Networks and its Security.
[2] J.Santhan Kumar Reddy“GIFITECHNOLOGY ”Gokula Krishna College of Enginnering.Report on “GI-FI Technology “.Issue 3 [ISSN 2250 - 3757].
[3] Shubham Chatterjee, Shalabh Agarwal, Asoke Nath, “scope and Challenges in Light Fidelity(LiFi)Technology in Wireless Data Communication”, International Journal of Innovative Research in Advanced Engineering(IJIRAE), Issue 6, Vol 2, Page 1-9,(June 2015).
[4] S. Vinay Kumar, K. Sudhakar, L. Sudha Rani (2014). Emerging Technology Li-Fi over Wi-Fi ,International Journal of Inventive Engineering and Sciences (IJIES), Vol. 2 Issue 3, February 2014.
[5] Sharma, R.R., Sanganal, A., Pati, S. (2014). Implementation of a Simple Li-Fi Based System, International Journal of Computing and Technology (IJCAT), Vol. 1 Issue 9, October 2014.
[6] Sridharan, C., Srikanth, P., Thresphine, J.R. (2014). Intelligence with Li-Fi Technology, International Journal of Computer Engineering & Science (IJCES), January 2014.
[7] Nivrutti, D.V., Nimbalkar, R.R. (2013). Light-Fidelity: A Reconnaissance of Future Technology, International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), Vol. 3 Issue 11, October 2013.
[8] Khandal, D., Jain, S. (2014). Li-Fi (Light Fidelity): The Future Technology in Wireless Communication, International Journal of Information & Computing Technology, Vol. 4 No. 16, 2014.

Sowbhagya M P, P Vikas Krishna, Darshan S, Nikhil A R, "Evolution of Gi-Fi and Li-Fi in Wireless Networks", International Journal of Computer Sciences and Engineering, Vol.04, Issue.03, pp.147-153, 2016.

This paper describes a new approach for advanced farming in agricultural management using Internet-of-Things, Mobile-Computing and Big-Data analytics. Farmers, marketing agencies and vendors need to be registered to the Farm-Cloud module through Mobile-App module. Farm-Cloud storage is used to store the details of farmers, soil properties of farmlands, vendors and marketing agencies, e-governance schemes and current environmental conditions. Soil and environment conditions are sensed and periodically sent to Farm-Cloud through IOT. Big-data analysis on Farm-Cloud data is done for fertilizer requirements, best crop sequences analysis, total production, and current stock and market requirements. Data Mining is used to solve the queries of vendors, farmers and marketing agencies through Farm-Cloud. Sensors are used to sense the details regarding the soil and its properties of the farmland and sends it to the Farm-Cloud. Proposed model is beneficial for increase in agricultural production and for cost control of products.

Internet-of-Things, Mobile Computing, Big-Data Analytics, Cloud Computing, Sensor, Smart Agriculture, Data Mining

[1] Xiaohui Wang and Nannan Liu, “The application of internet of things in agricultural means of production supply chain management ”, 2014
[2] Mo Lianguang ,“Study on Supply-chain of Modern Agricultural Products Based on IOT in Order to Guarantee the Quality and Safety ” ,2014
[3] Guohong Li, Wenjing Zhang,Yi Zhang, Hebei Langfang, “A Design of the IOT Gateway for Agricultural Greenhouse ” ,2014
[4] V.C. Patil, K.A. Al-Gaadi, D.P. Biradar, M. Rangaswamy, “INTERNET OF THINGS (IOT) AND CLOUD COMPUTING FOR AGRICULTURE: AN OVERVIEW” ,2012
[5] Ramya M G PG, Chetan Balaji, Girish L, “Environment Change Prediction to Adapt Climate Smart Agriculture Using Big Data Analytics ”,2015
[6] Nilesh Dumbre, Omkar Chikane, Gitesh, Dhangwadi,Savitribai Phule, “ SYSTEM FOR AGRICULTURE RECOMMENDATION USING DATA MINING ”,2015
[7] Fan TongKe, “Smart Agriculture Based on Cloud Computing and IOT ”,2013
[8] Changbo Ji,Hongyue Lu,Changqing Ji, Jingguo Yan, “An IoT and Mobile Cloud based Architecture for Smart Planting ” , 2015
[9] Ms.Kalpana.R,Dr.Shanthi.N, Dr.Arumugam.S, “ A Survey on Data Mining Techniques in Agriculture ”,2014
[10] Sirisha D, B Venkateswaramma, M Srikanth,A Anil Babu, “Wireless Sensor Based Remote Controlled Agriculture Monitoring System Using ZigBee ”,2015
[11] Chetan Dwarkani ,Ganesh Ram R ,Jagannathan ,R. Priyatharshini “Smart Farming System Using Sensors for Agricultural Task Automation ”,2015

Gireesh Babu C.N, Chandra Shekhara K.T , Kavitha S.A , Chethan D.N , "Advanced Farming Using Smart Technology", International Journal of Computer Sciences and Engineering, Vol.04, Issue.03, pp.154-158, 2016.

In this paper, the utilization of some of the large amount of data are inculcated to the users by some of the recommended products that are readily available. According to this the performance reliability of the particular product depends on the customers reviews so as to implement the relatively accessible cases in order to get the relevant manufactured articles that are attempting to get purchased from the customers who are ready to buy those products. The problem here is that the recommended system is being used only by the Amazon users. Therefore this paper is being presented for the utilization of other users also. Effectively the customer gets the reviews from the website in the same way in this paper the scanning of the barcode of the particular product is being introduced in order to inculcate the exact resources that are mandatorily introduced so as to perform the basic reviews of some of the similar products. Lastly the description of the product, name of the product, quality of the product needs as well as the summaries and reviews will be expected as the results.

Estimation Withdrawal, Recapitalization, User Reviews, Term Frequencies, Data manipulation, Synchronization, Customer evaluation.

[1] Alexandra Trilla, Francesc Alias "Condemnation-Pedestal Attitude scrutiny for Expressive Text-to-Speech", IEEE Transactions on Audio, Speech, and Language Progressioning, Vol. 21, No. 2, February 2013, pp.223-233.
[2] Alvaro Ortigosa, José M. Martín, Rosa M. Carro, "Attitude scrutiny in Facebook and its application to e-learning", Computers in Human Behavior Journal Elsevier 2013.
[3] Elena Lloret, Alexandra Balahur, José M. Gómez, Andrés Montoyo, Manuel Palomar, "Towards a unified framework for Estimation Repossesion, mining and summarization" Journal of Intelligent Information Systems Springer 2012, pp.711-747.
[4] Alexandra Balahur, Mijail Kabadjov, Josef Steinberger, Ralf Steinberger, Andrés Montoyo, "Challenges and solutions in the Estimation summarization", Journal of Intelligent Information Systems Springer 2012, pp.375-398.
[5] [5] Chien-Liang Liu, Wen-Hoar Hsaio, Chia-Hoang Lee, Gen-Chi Lu, and Emery Jou “Movie Rating and Re-evaluate Summarization in Mobile Upbringing”, IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Re-evaluates, Vol. 42, No. 3, May 2012, pp.397-406.
[6] Jingjing Liu, Stephanie Seneff, and Victor Zue, "Harvesting and Summarizing User-Spawnd Content for Advanced Speech-Pedestal HCI", IEEE Journal of Selected Topics in Signal Progressioning, Vol. 6, No. 8, Dec 2012, pp.982-992
[7] Chen-Huei Liao Effectiveness of Automated Chinese Sentence Scoring with Latent Semantic Analysis, April 2012
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[10] Bo Pang, Lillian Lee, "Opinion Mining and Sentiment Analysis", Foundations and Trends in Information Retrieval Vol. 2, Nos. 1–2 (2008).
[11] Esuli, A., & Sebastiani, F. (2006). “SentiWordNet: A publicly available resource for opinion mining”. In Proceedings of the 6th international conference on Language Resources and Evaluation (LREC’06), pp.417–422.

Kavyashree B L, Nandhish A C, "Estimating and Recapitalizing the Clients Reviews Efficiently on Snare", International Journal of Computer Sciences and Engineering, Vol.04, Issue.03, pp.159-164, 2016.

The Blue Brain Project is that the initial created comprehensive attempt to reverse engineer the brain of mammalian, so through elaborated simulations the perform of brain are often understood. BLUE BRAIN is that the name of the world’s initial virtual brain which suggests, a machine that may perform as human brain. Today, scientists square measure in analysis to form a synthetic brain that may suppose, respond, take call, and store something in memory. the most aim of this analysis is to transfer human brain into machine. so man will suppose and take call with none effort. This technology is often used for the event of human society.

Blue Brain, Artificial Neuron, Fuzzy Logic, Wetware Technology, Back Propagation

[1] The Blue Brain Project [online], (2005).
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[8] RC Chakraborty “ Fundamentals of Neural Networks”, myreaders .infohtml Artificial lintelligence .html,june 01,2010
[9] T. Kohonen, An Introduction to Neural Computing, Neural Networks, 1, (1988), 3-16

[10] Henry Markram builds a brain in a supercomputer TED Conference. July 2009.
[11] Reconstructing the Heart of Mammalian Intelligence
Henry Markram's Lecture, March 4, 2008.

[12] The Blue brain project, Hil, sean: Markram Henry, International conference of IEEE 2008.

Daina K K, Sindhura D, Vinod kumar R, Dhanalakshmi H.B, "Artificial Brain Using Wetware Technology and Fuzzy Logic", International Journal of Computer Sciences and Engineering, Vol.04, Issue.03, pp.165-169, 2016.

This paper describes some metrics to consider for web application development with frameworks like Google Web Toolkit and Google App Engine, technologies with a particular mode of operation and with some restrictions that affect the design and the functionality of these applications, but also offer great benefits such as improved user interface, better usability, improved performance, greater scalability and as the ability to use certain services, which allow application interoperability with different systems.

Web application, Java, GWT, App Engine, JDO, Design patterns, Model View Presenter.

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Vaibhavi Nayak, Vinuta V Naik ,Vijaykumar A S, "Development of Web Applications Using Google Technology", International Journal of Computer Sciences and Engineering, Vol.04, Issue.03, pp.170-174, 2016.

This paper provides an overview of important software engineering development process, user interface design tools, methods, application portability, quality and security, cost of development, hardware, software, licenses and developer accounts, proximity, Embrace minimalism.

Mobile devices, application development, software engineering, programming environments, user interface design, proximity, Embrace minimalism, closure,figure and ground and similarity.

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Raghavendra Rao B G and Vasantha S, "Software Engineering Development process, user interface design, methods and tools for Mobile Application Development", International Journal of Computer Sciences and Engineering, Vol.04, Issue.03, pp.174-178, 2016.

Detection of unusual events in video streams, for the purpose of investigation and security is a challenging technology in crowded scenes. To address this issues, an algorithm is proposed, which is based on Histogram of Optical Flow Orientation image descriptor and nonlinear one-class SVM classification method. The optical flow method is computed at each pixel to extract the low-level features. Histogram of Optical Flow Orientation descriptor encoding the global moving information of each frame and one-class support vector machine classifier detects the unusual events in the current frame, after learning period distinguishing the common behaviors of the training frame. k nearest neighbor classifier is used to classify the abnormal frames in video streams. Further, by combining the background subtraction step and optical flow computation, a improved version of the detection algorithm is designed. This proposed method works on several benchmark datasets to detect unusual events. Histogram of optical flow orientation with nonlinear one-class SVM classifier shows the high performance result than, k nearest neighbor classifier with histogram of optical flow orientation.

Unusual event detection, Optical Flow, Histogram of Optical Flow Orientation (HOFO), one-class SVM, k nearest neighbor (kNN).

[1] Tiang Wang and Hichem Snoussi,“Detection of abnormal visual events via global optical flow orientation histogram orientation histogram”, IEEE Trans.vol. 9. No 6, june 2014.
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Sujatha S, Alwyn Edison Mendonca, "Unusual Events Detection via Global Optical Flow and SVM", International Journal of Computer Sciences and Engineering, Vol.04, Issue.03, pp.179-183, 2016.

A Cognitive Radio is a Software Defined Radio (SDR).The Cognitive radio network is capable to sense and analyze its surrounding environment as well as reconfigure its operation in accordance with this radio environment. In this way, based on the available Channel State Information (CSI), the cognitive radio network may dynamically access the spectrum. MIMO based cognitive radio system enabled dynamically simultaneous usage of a radio spectrum for this beam forming signal processing technique is used. Beam forming is a technique in which the directionality of transmission and reception of radio signals can be controlled. Modern wireless technology depends on beam forming technology in order to provide higher data rates, improved coverage and also used to share the spectrum with the other users. Hardware complexity is one of the main issue in MIMO based wireless system which require N number of RF chains for N antenna systems. Antenna sub array formation (ASF) scheme is an optimization technique which can be used to reduce the RF chain required such a way the capacity can be improved. This will reduce the cost of the hardware much and we can realize low cost hardware system. Usually the two process of sub array formation and beam forming are done as separate process but in this paper the joint beam forming and sub array formation is done such a way the secondary user capacity will be improved and to avoid two computational complex process. Antenna sub array formation (ASF) scheme is employed to maximize the Signal to Interference Ratio (SINR) by using all antenna elements. In Antenna sub array formation the Radio Frequency chain is allocated to sub array of elements.

Antenna Selection(AS),antenna subarrayformation(ASF), beamforming ,cognitiveradio(CR) multiple-input multiple-output(MIMO)

[1] R. Zhang and Y. C. Liang, “Exploiting multi-antennas for opportunistic spectrum sharing in cognitive radio networks,” IEEE J. Sel. Topics Signal Process., vol-2, Issue- 1, pp. 88–102, Feb. 2008.

[2] S. Sanayei and A. Nosratinia, “Antenna selection in MIMO systems,”IEEE Commun.Mag., vol-42, Issue- 10, pp. 68–73, Oct. 2004.

[3] M. F. Hanif, P. J. Smith, D. P. Taylor, and P. A. Martin, “MIMO cognitive radios with antenna selection,” IEEE
Trans. Wireless Commun., vol.- 10, Issue-11, pp. 3688–
3699, Nov. 2011.

[4] P. D. Karamalis, N. D. Skentos, and A. G. Kanatas, “Adaptive antenna subarray formation for MIMO
systems,” IEEE Trans. Wireless Commun., vol.- 5, Issue-11,pp. 2977–2982, Nov. 2006.

[5] A. G. Kanatas, “A receive antenna subarray formation algorithm for MIMO systems,” IEEE Commun. Lett., vol.- 11, Issue- 5, pp. 396–398, May 2007.

[6] G. Zheng, S. Ma, K.-K. Wong, and T.-S. Ng, “Robust beamforming in cognitive radio,”IEEE Trans. Wireless Commun., vol. -9, Issue- 2, pp.570-576, Feb. 2010.

[7] Cheng-Xiang Wang, Senior Member, Xuemin Hong, Member, “On capacity of cognitive radio with average interference power constraint’’,IEEE Transactions On Wireless Comm, Vol.-8, Issue- 4, April 2009.

[8] Xinpeng Zeng,Quanshong Li,Qi Zhang,Jiayin Qin, ‘’Joint Beamforming and Antenna subarray formation for MIMO cognitive Radios’’, IEEE Signal Processing Letters, Vol- 20, Issue- 5, May 2013

Arpitha Shankar S, "MIMO Cognitive Radio with Low Cost Reception Using Beam Forming And Antenna Sub Array Formation", International Journal of Computer Sciences and Engineering, Vol.04, Issue.03, pp.184-187, 2016.

In this paper, we generate power through pressure based sensor. Proposal for the utilization of waste energy of foot power with human locomotion is very much relevant and important for highly populated countries like India and China where the roads, railway stations, bus stands, temples, etc. are all over crowded and millions of people move around the clock. This whole human/ bio energy being wasted if it can be made possible for utilization it will be great invention and crowd energy farms will be very useful energy sources in crowded countries In this project we are generating electrical power as non-conventional method by simply walking or running on the foot step. Non-conventional energy system is very essential at this time to our nation. Non-conventional energy using foot step is converting mechanical energy into the electrical energy. Power generation using conservative method becoming deficient. There is a necessity arises for a different power generation method. At the same time the energy is was due to human locomotion and many ways. To overcome this problem, the energy wastage can be converted to usable form using the Piezoelectric sensor. This sensor converts the pressure on it to a voltage. So by using this energy saving method, that is the Footstep Power Generation System we are generating power.

Pressure sensor, crowd farming,

[1] Richard, Michael Graham, (2006-08-04). "Japan: Producing Electricity from Train Station Ticket Gates". Tree Hugger. Discovery Communications, LLC.
[2] IEEE Standard on Piezoelectricity, Standards Committee of the IEEE Ultrasonic’s,
Ferroelectrics, and Frequency Control Society, ANSI/IEEE Std 176-1987 (1988).
[3] Becker, Robert O; Marino, Andrew A, (1982). "Chapter 4: Electrical Properties of Biological Tissue (Piezoelectricity)". Electromagnetism & Life. Albany, New York: State University of New York Press. ISBN 0-87395-560-9.
[4] Anil Kumar, International Journal of Scientific & Engineering Research Volume 2,
Issue 5, May-2011 ISSN 2229-5518.
[5] Andrew Townley, Electrical Engineering, University of Pennsylvania.
[6] Jedol Dayou, School of Science and Technology, University Malaysia Sabah, 88999 Kota
Kinabalu, Sabah, Malaysia. [7] Man-Sang, Faculty of Science, Art and Heritage, University Tun Hussein Onn Malaysia,
86400 Parit Raja, Batu Pahat, Johor, Malaysia.
[8] ANSI-IEEE 176 (1987) Standard on Piezoelectricity.
[9] S.Trolier-McKinstry,(2008)."Chapter3: Crystal Chemistry of Piezoelectric Materials". In A. Safari, E.K. Akdo˘gan. Piezoelectric and Acoustic Materials for Transducer Applications. New York: Springer. ISBN 9780387765389.

Divyakumar N, Ganesh V S , Vishnuraju G , Yogesh P and Sangappa S B, "Production of Electricity Through Pressure Based Sensors", International Journal of Computer Sciences and Engineering, Vol.04, Issue.03, pp.188-191, 2016.

In this paper, we propose an enhanced method for detecting light blobs (LBs) for Intelligent Headlight Control (IHC) using Digital Image Processing Techniques. The main function of the IHC system is to automatically convert high-beam headlights to low beam when vehicles are found in the vicinity. Thus, to implement the IHC, it is necessary to detect preceding or oncoming vehicles. This process of detecting vehicles is done by detecting LBs in the images. Here the algorithm is developed to analyze a frame and the same will be applied to all frames in a video. The area of interest is enhanced by converting the image to binary and thus detecting the LBs. To detect tail lights, red component of the image is extracted. Threshold value can be set depending on the object to be detected. Morphological operations will be performed on the binary image to remove all unwanted objects that are present in the image. Area of the light blob is calculated in the binary image and based on the value of this area, high beam is converted to low beam.

Light Blobs (LBs), Intelligent Headlight Control (IHC), Digital Image Processing, headlight, tail light, threshold value, morphological operations, binary image.

[1] Automatic headlight dimmer, a prototype for vehicles; Muralikrishnan, B.E, Electrical and Electronics Engineering, Sri Venkateswara College of Engineering, Tamil Nadu, India. IJRET: International Journal of Research in Engineering and Technology. eISSN: 2319-1163 | pISSN: 2321-7308
[2] “Digital Image Processing”, by Rafael Gonzalez 2012.
[3] MATLAB 2012b, by MathWorks, www.mathworks.com.
[4] Night-time Vehicle Detection for Intelligent Headlight Control. Antonio L´opez, J¨org Hilgenstock, Andreas Busse, Ram´on Baldrich, Felipe Lumbreras, and Joan Serrat, Computer Vision Centre and Computer Science Dept., Auton. Univ. of Barcelona, Volkswagen AG, Group Research, Carmeq GmbH, Business Team Surround Sensing, these authors are partially supported by Spanish MEC research projects Consolider Ingenio 2010: MIPRCV (CSD200700018) and TRA2007-62526/AUT. J. BlancTalon et al. (Eds.): ACIVS 2008, LNCS 5259, pp. 113–124, 2008. Springer-Verlag Berlin Heidelberg 2008
[5] A Novel Traffic-Tracking System Using Morphological and Blob Analysis : Prabhakar Telagarapu, Department of ECE, GMR Institute of Technology, RAJAM- 532 127, AP, INDIA, email:prabhakar.t@gmrit.org
[6] https://www.trw.com/integrated_systems/driver_assist_systems/forward_collision_warning
[7] https://www.researchgate.net/post/How_to_convert_MATLAB_code_to_C_program_in_Image_Processing
[8] Enhancing Light Blob Detection for Intelligent Headlight Control Using Lane Detection: Sungmin Eum, Member, IEEE, and Ho Gi Jung, Senior Member, IEEE (IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 14, NO. 2, JUNE 2013).

Amarjit Salam, Ashutosh Kumar, Mohammed Idris, Naman Narain and Sangappa S B, "Intelligent Headlight Control by Light Blob Detection", International Journal of Computer Sciences and Engineering, Vol.04, Issue.03, pp.192-196, 2016.